# The Enforcement and Feasibility of Hate Speech Moderation

arXiv: https://arxiv.org/abs/2604.12289 · HTML: https://mlsanity.com/paper/2604.12289/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 14 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media](https://mlsanity.com/paper/2305.16941/) (2023)
- [From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets](https://mlsanity.com/paper/2404.17874/) (2024)
- [NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helps](https://mlsanity.com/paper/2404.01651/) (2024)
- [Just Another Day on Twitter: A Complete 24 Hours of Twitter Data](https://mlsanity.com/paper/2301.11429/) (2023)
- [HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter](https://mlsanity.com/paper/2411.15462/) (2024)
- ["There Has To Be a Lot That We're Missing": Moderating AI-Generated Content on Reddit](https://mlsanity.com/paper/2311.12702/) (2023)
- [Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster](https://mlsanity.com/paper/2406.04106/) (2024)
- [How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events](https://mlsanity.com/paper/2209.07614/) (2022)
- [Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms](https://mlsanity.com/paper/2211.06516/) (2022)
- [Understanding the (In)Effectiveness of Content Moderation: A Case Study of Facebook in the Context of the U.S. Capitol Riot](https://mlsanity.com/paper/2301.02737/) (2023)
- [The Risks of Industry Influence in Tech Research](https://mlsanity.com/paper/2510.19894/) (2025)

## Built on by (most cited first, 1 of 1)

- [FBHM: Functional Benchmarking and Steering of VLMs for Hateful Meme Detection](https://mlsanity.com/paper/2605.31349/) (2026)

## Abstract (arXiv)

Online hate speech is associated with harms ranging from deteriorating mental health to violence, yet how consistently platforms moderate hate, and whether enforcement is feasible at scale, remain poorly understood. We audit hate speech moderation on Twitter (now X) using 540,000 tweets annotated by trained native speakers, representative of a full day on the platform. Five months after posting, 80% of hateful tweets, including violent ones, remained online. Removal was only marginally more likely than for non-hateful tweets, far below scams or adult content, and insensitive to severity and reach. Automated detection could not reliably classify hate but ranked it highly, enabling human triage. Simulating this workflow, current staffing curbed little exposure, yet substantial reductions proved financially feasible, far below applicable regulatory fines. Persistent hate reflects resource allocation, not technical limits.